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Abstract:

Three theories of analogy have been proposed which are supported by
computational models and data from experiments on human analogical abilities. In
this paper, we show how these theories can be unified within a common metatheoretical
framework which distinguishes between levels of informational,
behavioural and hardware constraints. This framework makes clear the
distinctions between three computational models in the literature (the Analogical
Constraint Mapping Engine, the Structure-Mapping Engine and the Incremental
Analogy Machine) . The paper then goes on to develop a methodology for the
comparative testing of these models. In two different manipulations of an
analogical-mapping task we compare the results of computational experiments with
these models against the results of psychological experiments. In the first
experiment, we show that increasing the number of similar elements in two
analogical domains, decreases the response time taken to reach the correct mapping
for an analogy problem. In the second psychological experiment we find that the
order in which the elements of the two domains are presented has significant
facilitative effects on the ease of analogical mapping. Only one of the three models,
that model which embodies behavioural constraints, the Incremental Analogy
Machine, predicts both of these results. Finally, the immediate implications of
these results for analogy research are discussed, along with the wider implications
the research has for cognitive science methodology.